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Distributed channel prediction for multi-agent systems

V.P. Chowdappa ; Markus Fröhle (Institutionen för signaler och system, Kommunikationssystem) ; Henk Wymeersch (Institutionen för signaler och system, Kommunikationssystem) ; C. Botella
2017 IEEE International Conference on Communications, ICC 2017, Paris, France, 21-25 May 2017 (1550-3607). (2017)
[Konferensbidrag, refereegranskat]

Multi-agent systems (MAS) communicate over a wireless network to coordinate their actions and to report their mission status. Connectivity and system-level performance can be improved by channel gain prediction. We present a distributed Gaussian process regression (GPR) framework for channel prediction in terms of the received power in MAS. The framework combines a Bayesian committee machine with an average consensus scheme, thus distributing not only the memory, but also computational and communication loads. Through Monte Carlo simulations, we demonstrate the performance of the proposed GPR.



Denna post skapades 2017-09-12.
CPL Pubid: 251829

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Kommunikationssystem (1900-2017)

Ämnesområden

Kommunikationssystem

Chalmers infrastruktur